Classification of granular materials via flowability-based clustering with application to bulk feeding

Feeder selection impacts the performance of bagging machinery throughout its life cycle, and yet it is usually based on qualitative assessments flowability. We propose a data analysis methodology aimed at verifying feeder-type classification powders grains by cluster their material properties. Results for first set conventional properties show granular materials clustered into as many groups main bulk feeding systems. Mismatch between feeder classes flowability-based clusters explained common industrial practice incomplete characterisation. For this reason, we introduce specialised measured with flow tester have recently developed. principal component second extended property that similarly flowing are better detected considering This research contributes to objectify decision-making process from quantitative description flow.